Generative Adversarial Networks (GANs) were called as the most interesting idea in the last 10 years in machine learning by Turing award recipient Yann LeCun. Their most significant impact has been observed in many challenging problems, such as plausible image generation, image-to-image translation, facial attribute manipulation and similar domains. However, there are still many research questions around GANs. For example, the minimax optimisation problem underlying GANs increases the training difficulty, how can we precisely manipulate the content creation in GANs, and whether the synthetic data from GANs can be reliable for the use in the finance or medical domains.
